Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations25634
Missing cells16556
Missing cells (%)5.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 MiB
Average record size in memory188.0 B

Variable types

Numeric8
Categorical4
DateTime1

Alerts

DIRECTION has constant value "A" Constant
POSITION_QC has constant value "1" Constant
TIME_QC has constant value "1" Constant
CYCLE_NUMBER is highly overall correlated with PLATFORM_NUMBERHigh correlation
DATA_MODE is highly overall correlated with LATITUDE and 1 other fieldsHigh correlation
LATITUDE is highly overall correlated with DATA_MODE and 1 other fieldsHigh correlation
LONGITUDE is highly overall correlated with DATA_MODE and 1 other fieldsHigh correlation
PLATFORM_NUMBER is highly overall correlated with CYCLE_NUMBERHigh correlation
PRES is highly overall correlated with TEMPHigh correlation
TEMP is highly overall correlated with PRESHigh correlation
PSAL has 8278 (32.3%) missing values Missing
TEMP has 8278 (32.3%) missing values Missing
N_POINTS is uniformly distributed Uniform
N_POINTS has unique values Unique

Reproduction

Analysis started2025-09-20 17:32:25.882687
Analysis finished2025-09-20 17:32:38.094657
Duration12.21 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

N_POINTS
Real number (ℝ)

Uniform  Unique 

Distinct25634
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12816.5
Minimum0
Maximum25633
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size200.4 KiB
2025-09-20T17:32:38.208320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1281.65
Q16408.25
median12816.5
Q319224.75
95-th percentile24351.35
Maximum25633
Range25633
Interquartile range (IQR)12816.5

Descriptive statistics

Standard deviation7400.0427
Coefficient of variation (CV)0.57738405
Kurtosis-1.2
Mean12816.5
Median Absolute Deviation (MAD)6408.5
Skewness0
Sum3.2853816 × 108
Variance54760632
MonotonicityStrictly increasing
2025-09-20T17:32:38.344134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25633 1
 
< 0.1%
0 1
 
< 0.1%
25617 1
 
< 0.1%
25616 1
 
< 0.1%
25615 1
 
< 0.1%
25614 1
 
< 0.1%
25613 1
 
< 0.1%
25612 1
 
< 0.1%
25611 1
 
< 0.1%
25610 1
 
< 0.1%
Other values (25624) 25624
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
25633 1
< 0.1%
25632 1
< 0.1%
25631 1
< 0.1%
25630 1
< 0.1%
25629 1
< 0.1%
25628 1
< 0.1%
25627 1
< 0.1%
25626 1
< 0.1%
25625 1
< 0.1%
25624 1
< 0.1%

CYCLE_NUMBER
Real number (ℝ)

High correlation 

Distinct46
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.639502
Minimum4
Maximum264
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size200.4 KiB
2025-09-20T17:32:38.494336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6
Q127
median38
Q387
95-th percentile262
Maximum264
Range260
Interquartile range (IQR)60

Descriptive statistics

Standard deviation63.839455
Coefficient of variation (CV)1.003142
Kurtosis3.8131165
Mean63.639502
Median Absolute Deviation (MAD)16
Skewness2.1391447
Sum1631335
Variance4075.476
MonotonicityNot monotonic
2025-09-20T17:32:38.626445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
87 1383
 
5.4%
88 1381
 
5.4%
89 1352
 
5.3%
20 1181
 
4.6%
51 1157
 
4.5%
52 1157
 
4.5%
22 1112
 
4.3%
21 1110
 
4.3%
37 1059
 
4.1%
36 1058
 
4.1%
Other values (36) 13684
53.4%
ValueCountFrequency (%)
4 490
1.9%
5 490
1.9%
6 490
1.9%
18 62
 
0.2%
19 97
 
0.4%
20 1181
4.6%
21 1110
4.3%
22 1112
4.3%
23 62
 
0.2%
24 277
 
1.1%
ValueCountFrequency (%)
264 499
1.9%
263 498
1.9%
262 498
1.9%
246 62
 
0.2%
245 61
 
0.2%
244 61
 
0.2%
210 51
 
0.2%
205 60
 
0.2%
204 124
 
0.5%
203 246
1.0%

DATA_MODE
Categorical

High correlation 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
R
17990 
A
7318 
D
 
326

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25634
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowR
2nd rowR
3rd rowR
4th rowR
5th rowR

Common Values

ValueCountFrequency (%)
R 17990
70.2%
A 7318
28.5%
D 326
 
1.3%

Length

2025-09-20T17:32:38.742301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-20T17:32:38.826437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
r 17990
70.2%
a 7318
28.5%
d 326
 
1.3%

Most occurring characters

ValueCountFrequency (%)
R 17990
70.2%
A 7318
28.5%
D 326
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25634
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 17990
70.2%
A 7318
28.5%
D 326
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25634
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 17990
70.2%
A 7318
28.5%
D 326
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25634
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 17990
70.2%
A 7318
28.5%
D 326
 
1.3%

DIRECTION
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
A
25634 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25634
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 25634
100.0%

Length

2025-09-20T17:32:38.908214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-20T17:32:38.964591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
a 25634
100.0%

Most occurring characters

ValueCountFrequency (%)
A 25634
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25634
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 25634
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25634
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 25634
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25634
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 25634
100.0%

PLATFORM_NUMBER
Real number (ℝ)

High correlation 

Distinct39
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3263270.4
Minimum1902198
Maximum7902251
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size200.4 KiB
2025-09-20T17:32:39.039855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1902198
5-th percentile1902198
Q11902594
median2903829
Q32903989
95-th percentile6990608
Maximum7902251
Range6000053
Interquartile range (IQR)1001395

Descriptive statistics

Standard deviation1671836.1
Coefficient of variation (CV)0.5123192
Kurtosis0.48347209
Mean3263270.4
Median Absolute Deviation (MAD)1001235
Skewness1.25737
Sum8.3650673 × 1010
Variance2.7950359 × 1012
MonotonicityNot monotonic
2025-09-20T17:32:39.157941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
1902594 4116
16.1%
2903829 3175
12.4%
1902373 3164
12.3%
2903831 2993
11.7%
2903434 1515
 
5.9%
1902198 1495
 
5.8%
5907152 1470
 
5.7%
5907086 978
 
3.8%
1902681 978
 
3.8%
4903794 877
 
3.4%
Other values (29) 4873
19.0%
ValueCountFrequency (%)
1902198 1495
 
5.8%
1902373 3164
12.3%
1902594 4116
16.1%
1902669 176
 
0.7%
1902670 186
 
0.7%
1902681 978
 
3.8%
2902765 183
 
0.7%
2902766 51
 
0.2%
2902768 184
 
0.7%
2902770 183
 
0.7%
ValueCountFrequency (%)
7902251 186
0.7%
7902249 186
0.7%
7902248 153
0.6%
7901127 55
 
0.2%
7901126 167
0.7%
7901125 179
0.7%
6990705 124
0.5%
6990609 123
0.5%
6990608 124
0.5%
5907171 61
 
0.2%

POSITION_QC
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
1
25634 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25634
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 25634
100.0%

Length

2025-09-20T17:32:39.551464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-20T17:32:39.609348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 25634
100.0%

Most occurring characters

ValueCountFrequency (%)
1 25634
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25634
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 25634
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25634
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 25634
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25634
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 25634
100.0%

PRES
Real number (ℝ)

High correlation 

Distinct12054
Distinct (%)47.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean418.23766
Minimum0
Maximum999.96002
Zeros7
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size200.4 KiB
2025-09-20T17:32:39.702022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.04
Q1155.10001
median365.8
Q3673.54002
95-th percentile935.76001
Maximum999.96002
Range999.96002
Interquartile range (IQR)518.44002

Descriptive statistics

Standard deviation298.78588
Coefficient of variation (CV)0.71439258
Kurtosis-1.1571544
Mean418.23766
Median Absolute Deviation (MAD)246.795
Skewness0.3344048
Sum10721104
Variance89273.004
MonotonicityNot monotonic
2025-09-20T17:32:39.839895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 26
 
0.1%
538 24
 
0.1%
3 24
 
0.1%
6 23
 
0.1%
788 22
 
0.1%
7 21
 
0.1%
1 21
 
0.1%
588 21
 
0.1%
10 20
 
0.1%
687.799988 20
 
0.1%
Other values (12044) 25412
99.1%
ValueCountFrequency (%)
0 7
 
< 0.1%
0.09 1
 
< 0.1%
0.1 17
0.1%
0.19 1
 
< 0.1%
0.2 17
0.1%
0.29 1
 
< 0.1%
0.3 18
0.1%
0.36 1
 
< 0.1%
0.38 1
 
< 0.1%
0.4 13
0.1%
ValueCountFrequency (%)
999.960022 7
< 0.1%
999.859985 1
 
< 0.1%
999.800049 1
 
< 0.1%
999.799988 1
 
< 0.1%
999.76001 2
 
< 0.1%
999.719971 3
< 0.1%
999.700012 2
 
< 0.1%
999.599976 1
 
< 0.1%
999.52002 1
 
< 0.1%
999.47998 2
 
< 0.1%

PSAL
Real number (ℝ)

Missing 

Distinct3706
Distinct (%)21.4%
Missing8278
Missing (%)32.3%
Infinite0
Infinite (%)0.0%
Mean30.586277
Minimum0.1
Maximum35.136002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size200.4 KiB
2025-09-20T17:32:39.973015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.108
Q133.973
median34.959202
Q335.018002
95-th percentile35.046001
Maximum35.136002
Range35.036002
Interquartile range (IQR)1.045002

Descriptive statistics

Standard deviation10.21078
Coefficient of variation (CV)0.33383533
Kurtosis4.0655145
Mean30.586277
Median Absolute Deviation (MAD)0.074596
Skewness-2.3554594
Sum530855.42
Variance104.26003
MonotonicityNot monotonic
2025-09-20T17:32:40.111590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.030998 138
 
0.5%
35.034 136
 
0.5%
35.035 136
 
0.5%
0.106 130
 
0.5%
35.032001 129
 
0.5%
0.107 126
 
0.5%
35.033001 123
 
0.5%
0.103 118
 
0.5%
35.029999 114
 
0.4%
0.105 113
 
0.4%
Other values (3696) 16093
62.8%
(Missing) 8278
32.3%
ValueCountFrequency (%)
0.1 77
0.3%
0.101 82
0.3%
0.102 110
0.4%
0.103 118
0.5%
0.104 104
0.4%
0.105 113
0.4%
0.106 130
0.5%
0.107 126
0.5%
0.108 107
0.4%
0.109 88
0.3%
ValueCountFrequency (%)
35.136002 1
< 0.1%
35.118999 1
< 0.1%
35.117001 1
< 0.1%
35.115002 1
< 0.1%
35.112 1
< 0.1%
35.109001 1
< 0.1%
35.104 1
< 0.1%
35.101002 1
< 0.1%
35.099998 1
< 0.1%
35.098999 1
< 0.1%

TEMP
Real number (ℝ)

High correlation  Missing 

Distinct9845
Distinct (%)56.7%
Missing8278
Missing (%)32.3%
Infinite0
Infinite (%)0.0%
Mean13.957085
Minimum6.492
Maximum30.003
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size200.4 KiB
2025-09-20T17:32:40.250898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6.492
5-th percentile7.16275
Q18.66575
median10.899
Q316.889
95-th percentile29.10525
Maximum30.003
Range23.511
Interquartile range (IQR)8.22325

Descriptive statistics

Standard deviation7.2714026
Coefficient of variation (CV)0.52098288
Kurtosis-0.14664205
Mean13.957085
Median Absolute Deviation (MAD)2.794
Skewness1.1391301
Sum242239.18
Variance52.873295
MonotonicityNot monotonic
2025-09-20T17:32:40.436883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.030001 16
 
0.1%
28.867001 14
 
0.1%
28.951 13
 
0.1%
28.799999 12
 
< 0.1%
29.316 11
 
< 0.1%
29.056 11
 
< 0.1%
29.027 11
 
< 0.1%
29.028999 11
 
< 0.1%
29.069 9
 
< 0.1%
29.108 9
 
< 0.1%
Other values (9835) 17239
67.3%
(Missing) 8278
32.3%
ValueCountFrequency (%)
6.492 1
< 0.1%
6.5 1
< 0.1%
6.512 1
< 0.1%
6.516 1
< 0.1%
6.518 1
< 0.1%
6.528 1
< 0.1%
6.53 1
< 0.1%
6.5363 1
< 0.1%
6.538 1
< 0.1%
6.539 1
< 0.1%
ValueCountFrequency (%)
30.003 1
< 0.1%
30.000999 1
< 0.1%
30 1
< 0.1%
29.997999 2
< 0.1%
29.995001 2
< 0.1%
29.993 1
< 0.1%
29.992001 1
< 0.1%
29.990999 1
< 0.1%
29.974001 1
< 0.1%
29.879999 2
< 0.1%

TIME_QC
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
1
25634 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters25634
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 25634
100.0%

Length

2025-09-20T17:32:40.615278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-09-20T17:32:40.706749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 25634
100.0%

Most occurring characters

ValueCountFrequency (%)
1 25634
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25634
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 25634
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25634
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 25634
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25634
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 25634
100.0%

TIME
Date

Distinct105
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size200.4 KiB
Minimum2025-08-20 14:05:55+00:00
Maximum2025-09-19 14:18:22.999000+00:00
Invalid dates0
Invalid dates (%)0.0%
2025-09-20T17:32:40.833448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:41.034082image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

LATITUDE
Real number (ℝ)

High correlation 

Distinct100
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.520403
Minimum5.1882
Maximum17.66628
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size200.4 KiB
2025-09-20T17:32:41.222552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.1882
5-th percentile5.4722
Q18.04032
median11.133333
Q315.08003
95-th percentile17.21747
Maximum17.66628
Range12.47808
Interquartile range (IQR)7.03971

Descriptive statistics

Standard deviation3.8242014
Coefficient of variation (CV)0.33195033
Kurtosis-1.2529431
Mean11.520403
Median Absolute Deviation (MAD)3.3336667
Skewness-0.0020251743
Sum295314
Variance14.624517
MonotonicityNot monotonic
2025-09-20T17:32:41.417293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.254466 1383
 
5.4%
10.33679233 1381
 
5.4%
10.20384567 1352
 
5.3%
8.4755 1059
 
4.1%
15.08003 1059
 
4.1%
8.04032 1058
 
4.1%
15.52375 1058
 
4.1%
7.88894 1058
 
4.1%
14.80746 1047
 
4.1%
16.73191 999
 
3.9%
Other values (90) 14180
55.3%
ValueCountFrequency (%)
5.1882 498
1.9%
5.4003 499
1.9%
5.4722 498
1.9%
5.576895 326
1.3%
5.70228 326
1.3%
5.714376667 326
1.3%
6.245886667 292
1.1%
6.483333333 62
 
0.2%
6.53263 292
1.1%
6.65 62
 
0.2%
ValueCountFrequency (%)
17.66628 999
3.9%
17.21747 995
3.9%
16.93333333 35
 
0.1%
16.926 51
 
0.2%
16.73191 999
3.9%
16.13333333 115
 
0.4%
16.1203 490
1.9%
16.1 53
 
0.2%
16.0889 490
1.9%
16.0757 490
1.9%

LONGITUDE
Real number (ℝ)

High correlation 

Distinct102
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.233244
Minimum80.35087
Maximum91.816667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size200.4 KiB
2025-09-20T17:32:41.608685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum80.35087
5-th percentile83.9987
Q185.918767
median86.684251
Q389.016667
95-th percentile90.996702
Maximum91.816667
Range11.465797
Interquartile range (IQR)3.0979

Descriptive statistics

Standard deviation2.0968287
Coefficient of variation (CV)0.024037036
Kurtosis0.13823499
Mean87.233244
Median Absolute Deviation (MAD)1.6155993
Skewness-0.12582827
Sum2236137
Variance4.3966904
MonotonicityNot monotonic
2025-09-20T17:32:41.840874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
86.93314217 1383
 
5.4%
86.68425067 1381
 
5.4%
86.3838625 1352
 
5.3%
85.99619 1059
 
4.1%
88.46278 1059
 
4.1%
85.66807 1058
 
4.1%
88.60382 1058
 
4.1%
85.83524 1058
 
4.1%
88.29985 1047
 
4.1%
89.25651 999
 
3.9%
Other values (92) 14180
55.3%
ValueCountFrequency (%)
80.35087 62
 
0.2%
80.41666667 62
 
0.2%
80.6 62
 
0.2%
81.002 62
 
0.2%
81.047 62
 
0.2%
81.282 60
 
0.2%
83.2821 498
1.9%
83.51666667 62
 
0.2%
83.53333333 62
 
0.2%
83.86666667 62
 
0.2%
ValueCountFrequency (%)
91.81666667 43
 
0.2%
91.76666667 62
 
0.2%
91.66666667 62
 
0.2%
91.589 62
 
0.2%
91.448 60
 
0.2%
91.3 20
 
0.1%
91.25873167 292
1.1%
91.152 61
 
0.2%
91.13494167 293
1.1%
91.13333333 35
 
0.1%

Interactions

2025-09-20T17:32:36.653559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:27.731679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:30.681869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:31.983588image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:32.910534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:33.799314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:34.691619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:35.751233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:36.771092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:28.198903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:31.233222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:32.096620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:33.020256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:33.906385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:35.025491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:35.860587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:36.880690image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:28.547234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:31.333575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:32.229093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:33.122503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:34.012448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:35.129096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:35.967087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:37.000703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:28.981368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:31.438687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:32.340666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:33.257537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:34.123189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:35.234998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:36.079386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:37.116891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:29.272051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:31.539129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:32.448627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:33.354947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:34.234564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:35.349536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:36.189104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:37.246818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:29.709217image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:31.649312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:32.564131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:33.473270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:34.371379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:35.451214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:36.302701image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:37.357952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:30.107795image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:31.758850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:32.673566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:33.583629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:34.471056image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:35.545489image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:36.428073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:37.484454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:30.444553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:31.868008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:32.788319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:33.698061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:34.580977image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:35.647998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-09-20T17:32:36.537083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-09-20T17:32:41.993263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
CYCLE_NUMBERDATA_MODELATITUDELONGITUDEN_POINTSPLATFORM_NUMBERPRESPSALTEMP
CYCLE_NUMBER1.0000.454-0.295-0.3000.155-0.571-0.001-0.3210.082
DATA_MODE0.4541.0000.5870.5100.4410.4420.0220.3070.042
LATITUDE-0.2950.5871.0000.5780.0920.0800.0110.122-0.070
LONGITUDE-0.3000.5100.5781.0000.0200.306-0.0070.218-0.002
N_POINTS0.1550.4410.0920.0201.000-0.0770.0220.014-0.016
PLATFORM_NUMBER-0.5710.4420.0800.306-0.0771.000-0.0590.2550.079
PRES-0.0010.0220.011-0.0070.022-0.0591.0000.200-0.997
PSAL-0.3210.3070.1220.2180.0140.2550.2001.000-0.199
TEMP0.0820.042-0.070-0.002-0.0160.079-0.997-0.1991.000

Missing values

2025-09-20T17:32:37.646071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-09-20T17:32:37.805087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-09-20T17:32:38.017040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

N_POINTSCYCLE_NUMBERDATA_MODEDIRECTIONPLATFORM_NUMBERPOSITION_QCPRESPSALTEMPTIME_QCTIMELATITUDELONGITUDE
0071RA790112510.232.99100129.13699912025-08-20 14:05:55+00:0012.4591.066667
1171RA790112511.132.98899829.13900012025-08-20 14:05:55+00:0012.4591.066667
2271RA790112512.132.98899829.13900012025-08-20 14:05:55+00:0012.4591.066667
3371RA790112512.832.98500129.14500012025-08-20 14:05:55+00:0012.4591.066667
4471RA790112514.032.98700029.14100112025-08-20 14:05:55+00:0012.4591.066667
5571RA790112515.332.99100129.14399912025-08-20 14:05:55+00:0012.4591.066667
6671RA790112516.132.98899829.14399912025-08-20 14:05:55+00:0012.4591.066667
7771RA790112517.032.98799929.14500012025-08-20 14:05:55+00:0012.4591.066667
8871RA790112517.832.98799929.14200012025-08-20 14:05:55+00:0012.4591.066667
9971RA790112518.732.99000229.14699912025-08-20 14:05:55+00:0012.4591.066667
N_POINTSCYCLE_NUMBERDATA_MODEDIRECTIONPLATFORM_NUMBERPOSITION_QCPRESPSALTEMPTIME_QCTIMELATITUDELONGITUDE
256242562474RA7901125185.09999834.49499924.03700112025-09-19 14:18:22.999000+00:0012.690.833333
256252562574RA7901125195.50000034.57400122.92400012025-09-19 14:18:22.999000+00:0012.690.833333
256262562674RA79011251105.50000034.56000121.58900112025-09-19 14:18:22.999000+00:0012.690.833333
256272562774RA79011251115.09999834.63299920.12299912025-09-19 14:18:22.999000+00:0012.690.833333
256282562874RA79011251125.19999734.68999918.89300012025-09-19 14:18:22.999000+00:0012.690.833333
256292562974RA79011251135.39999434.70900018.54100012025-09-19 14:18:22.999000+00:0012.690.833333
256302563074RA79011251145.50000034.74700217.99700012025-09-19 14:18:22.999000+00:0012.690.833333
256312563174RA79011251155.00000034.77700017.50799912025-09-19 14:18:22.999000+00:0012.690.833333
256322563274RA79011251165.10000634.84199916.68199912025-09-19 14:18:22.999000+00:0012.690.833333
256332563374RA79011251175.10000634.89699915.81100012025-09-19 14:18:22.999000+00:0012.690.833333